Analytics Project 2025: Self-Checkout Fraud

Data Understanding and Exploration

This document presents an initial exploratory analysis of the data set related to self-checkout fraud detection. The focus is on understanding the structure of the fraud.csv data set before proceeding with further analysis.

Loading and Inspection of the Data

Code

##| echo: true #This can be added to selectively show specific code chunks.
# Load dataset
data_path <- file.path(dirname(dirname(here())), "Data", "fraud.csv")
df <- fread(data_path)

Dimensions of the Dataset

Code

# Dimensions of the dataset
cat("The dataset contains", num_rows, "rows and", num_cols, "columns.\n")

The dataset contains 498121 rows and 10 columns.

Summary of the whole Dataset

Code

skim(df)

Data summary

Name	df
Number of rows	498121
Number of columns	10
Key	NULL
_______________________
Column type frequency:
numeric	10
________________________
Group variables	None

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
trustLevel	0	1	3.50	1.71	1	2.00	4.00	5.00	6.00	▇▅▅▅▅
totalScanTimeInSeconds	0	1	915.61	528.77	1	458.00	916.00	1374.00	1831.00	▇▇▇▇▇
grandTotal	0	1	49.99	28.87	0	24.93	50.03	75.02	99.99	▇▇▇▇▇
lineItemVoids	0	1	5.50	3.45	0	3.00	5.00	8.00	11.00	▇▆▆▅▇
scansWithoutRegistration	0	1	5.00	3.16	0	2.00	5.00	8.00	10.00	▇▅▅▅▆
quantityModifications	0	1	2.50	1.71	0	1.00	2.00	4.00	5.00	▇▃▃▅▃
scannedLineItemsPerSecond	0	1	0.07	0.52	0	0.01	0.02	0.03	30.00	▇▁▁▁▁
valuePerSecond	0	1	0.22	1.72	0	0.03	0.05	0.11	99.71	▇▁▁▁▁
lineItemVoidsPerPosition	0	1	0.74	1.32	0	0.16	0.35	0.69	11.00	▇▁▁▁▁
fraud	0	1	0.05	0.21	0	0.00	0.00	0.00	1.00	▇▁▁▁▁

Underlying Data Types

Code

cat(paste(names(df), "➡️", sapply(df, class)), sep = "\n")

trustLevel ➡️ integer
totalScanTimeInSeconds ➡️ integer
grandTotal ➡️ numeric
lineItemVoids ➡️ integer
scansWithoutRegistration ➡️ integer
quantityModifications ➡️ integer
scannedLineItemsPerSecond ➡️ numeric
valuePerSecond ➡️ numeric
lineItemVoidsPerPosition ➡️ numeric
fraud ➡️ integer

Checking for missing values and duplicates

Code

cat("The dataset contains", sum(missing_values), "missing values and ", duplicate_count, " duplicates.\n")

The dataset contains 0 missing values and  0  duplicates.

Correlation Matrix

Code

#Creation of correlation matrix
cor_matrix <- cor(df, use = "complete.obs") #Handle missing values if present.
#print(cor_matrix)

#Visualization usng corrplot
corrplot(cor_matrix, method = "color", col = COL1("YlOrRd"),
         tl.col = "grey30", tl.srt = 45, tl.cex = 0.675 ,addCoef.col = "white", 
         number.cex = 0.8, addgrid.col = "white")

Data Exploration of all features

Variation of the target variable “fraud”

Code

# Create the bar chart with count labels
t_hist <- ggplot(df, aes(x = factor(fraud))) +  
  geom_bar(fill = "steelblue", alpha = 0.7) +  
  geom_text(stat = "count", aes(label =  after_stat(count)), size = 5) +  
  labs(title = "Distribution of Variable \"Fraud\"",
       x = "Fraud",
       y = "Count") +
  theme_minimal()

ggplotly(t_hist)  # Display static plot

Code

#Redundant??? Imo --> Ronny 

t_hist <- ggplot(df, aes(x = factor(trustLevel))) +  # Categorical variable
            geom_bar(fill = "steelblue", color = "black", alpha = 0.7) +
            labs(title = "Distribution of Variable \"trustLevel\"",
              x = "Trust Level",
              y = "Count")

ggplotly(t_hist)

Various exploration of the remaining features with Fraud Breakdown

1. A breakdown of fraudulent transaction per trustLevel

Code

# Create the stacked bar chart
t_hist <- ggplot(df, aes(x = factor(trustLevel), fill = factor(fraud))) +  
  geom_bar(position = "stack", alpha = 0.7) +  
  labs(title = "Trust Level Distribution with Fraud Breakdown",
       x = "Trust Level",
       y = "Count",
       fill = "Fraud Status") +
  scale_fill_manual(values = c("0" = "steelblue", "1" = "red")) + # Custom colors
  theme_minimal()

# Interactive version
ggplotly(t_hist)

2. A breakdown of totalScanTimeInSeconds with Fraud Breakdown

Code

# Boxplot for totalScanTimeInSeconds by fraud status
boxplot_scan_time <- ggplot(df, aes(x = factor(fraud), y = totalScanTimeInSeconds, fill = factor(fraud))) +
  geom_boxplot(alpha = 0.7, outlier.color = "red") +  
  labs(title = "Total Scan Time by Fraud Status",
       x = "Fraud Status",
       y = "Total Scan Time (Seconds)",
       fill = "Fraud Status") +
  scale_fill_manual(values = c("0" = "steelblue", "1" = "red")) +  
  theme_minimal()

ggplotly(boxplot_scan_time)  # Interactive version

Code

# Density plot for totalScanTimeInSeconds by fraud status
density_scan_time <- ggplot(df, aes(x = totalScanTimeInSeconds, fill = factor(fraud))) +
  geom_density(alpha = 0.5) +
  labs(title = "Density Plot of Total Scan Time by Fraud Status",
       x = "Total Scan Time (Seconds)",
       y = "Density",
       fill = "Fraud Status") +
  scale_fill_manual(values = c("0" = "steelblue", "1" = "red")) +
  theme_minimal()

ggplotly(density_scan_time)  # Interactive version

3. A breakdown of grandTotal with Fraud Breakdown

Code

# Boxplot for grandTotal by fraud status
boxplot_grand_total <- ggplot(df, aes(x = factor(fraud), y = grandTotal, fill = factor(fraud))) +
  geom_boxplot(alpha = 0.7, outlier.color = "red") +  
  labs(title = "Grand Total by Fraud Status",
       x = "Fraud Status",
       y = "Transaction Amount (Grand Total)",
       fill = "Fraud Status") +
  scale_fill_manual(values = c("0" = "steelblue", "1" = "red")) +  
  theme_minimal()

ggplotly(boxplot_grand_total)  # Interactive version